Abstract
We propose combining advanced statistical approaches with data mining techniques to build classifiers to enhance decision-making models for the job assignment problem. Adaptive Generalized Estimation Equation (AGEE) approaches with Gibbs sampling under Bayesian framework and adaptive Bayes classifiers based on the estimations of AGEE models which uses modified Naive Bayes algorithm are proposed. The proposed classifiers have several important features. Firstly, it accounts for the correlation among the outputs and the indeterministic subjective noise into the estimation of parameters. Secondly, it reduces the number of attributes used to predict the class. Moreover, it drops the assumption of independence made by the Naive Bayes classifier. We apply our techniques to the problem of assigning jobs to Navy officers, with the goal of enhancing happiness for both the Navy and the officers. The classification results were compared with nearest neighbor, Multi-Layer Perceptron and Support Vector Machine approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fraley, C, Raftery, A. E.: Model-Based Clustering, Discriminant Analysis, and Density Estimation, Technical Report NO.380, 2000.
Liang, K. Y., Zeger S. L.: Longitudinal Data Analysis Using Generalized Linear Models, Biometrika, 73, 13–22, 1986.
Keller, A. D., Schummer, M., Hood L., Ruzzo W. L.: Bayesian Classification of DNA Array Expression Data, Technical Report UW-CSE-2000-08-01, 2000.
Horton, N. J., Lipsitz, S. R.: Review of Software to Fit Generalized Estimation Equation Regression Models, The American Statistician, Vol. 53, May 1999.
Albert L, Jais, J. P.: Gibbs Sampler for the Logistic Model In the Analysis of Longitudinal Binary Data, Statist. Med.17, 2905–2921, 1998
Friedman, J., Hastie T., Tibshirani, R.: Additive Logistic Regression: a Statistical View of Boosting. Technical Report, 2000.
Dietterich, T. G.: Ensemble methods in machine learning, In Multiple Classier Systems. First International Workshop, MCS 2000, Cagliari, Italy, pages 1–15. Springer-Verlag, 2000.
Akaike, H.: A new look at the statistical model identification, IEEE Trans. Automatic Control, Vol. 19, No. 6, pp. 716–723, 1974.
Cortes C, Vapnik, V.: Support vector machines, Machine Learning, 20, pp. 273–297, 1995.
Friess, T. T., Cristianini N., Campbell, C: The kernel adatron algorithm: a fast and simple learning procedure for support vector machine, In Proc. 15th International Conference on Machine Learning, Morgan Kaufman Publishers, 1998.
Boser, B., Guyon, I., Vapnik, V.: A Training Algorithm for Optimal Margin Classifiers, In Proceedings of the Fifth Workshop on Computational Learning Theory, pp. 144–152, 1992.
Gelfand, A. E., Hills, S. E., Racine-Poon, A., Smith, A. F. M.: Illustration of Bayesian inference in normal data models using Gibbs sampling. Journal of the American Statistical Association, 85(412):pp. 972–985, 1990.
Spiegelman, D., Rosner B., Logan, R.: Estimation and Inference for logistic Regression with Covariate Misclassification and Measurement Error in main Study/Validation Study Designs, Journal of the American Statistical Association, 95(449): pp. 51–61, 2000.
Han, J., Kamber, M.: Data Mining Concepts and Techniques, 2000.
Stone, M.: Cross-validatory choice and assessment of statistical predictions (with discussion), Journal of the Royal Statistical Society, Series B, 36, pp. 111–147, 1974.
Kondadadi, R., Dasgupta, D., Franklin, S.: An Evolutionary Approach For Job Assignment, Proceedings of International Conference on Intelligent Systems-2000, Louisville, Kentucky, 2000.
Liang T. T., Thompson, T. J.: Applications and Implementation-A large-scale personnel assignment model for the Navy, The Journal For The Decisions Sciences Institute, Volume 18, No. 2 Spring, 1987.
Gale D., Shapley, L. S.: College Admissions and stability of marriage, The American Mathematical monthly, Vol 60, No 1, pp. 9–15, 1962.
Franklin, S., Kelemen, A., McCauley, L.: IDA: A cognitive agent architecture, In the proceedings of IEEE International Conference on Systems, Man, and Cybernetics’ 98, IEEE Press, pp. 2646, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liang, Y., Lin, KI., Kelemen, A. (2002). Adaptive Generalized Estimation Equation with Bayes Classifier for the Job Assignment Problem. In: Chen, MS., Yu, P.S., Liu, B. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2002. Lecture Notes in Computer Science(), vol 2336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47887-6_43
Download citation
DOI: https://doi.org/10.1007/3-540-47887-6_43
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43704-8
Online ISBN: 978-3-540-47887-4
eBook Packages: Springer Book Archive